What's stopping Cepheus from generalizing to full poker games?
The reason why Cepheus can't generalize has to do with the number of decision points.

The same authors recently let loose Deep Stack (DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit (HUNL) Poker) which is freaking many professional poker players out.

In the DeepStack arxiv paper, they say


  AI techniques (Cepheus) have previously shown
  success in the simpler game of heads-up limit Texas hold’em, where all bets are of a fixed size resulting in just under 10^14 decision points.


...


  The imperfect information game HUNL is comparable in size to go, with
  the number of decision points exceeding 10^160


...


  Imperfect information games require more complex reasoning than similarly sized perfect
  information games. The correct decision at a particular moment depends upon the probability
  distribution over private information that the opponent holds, which is revealed through their
  past actions.


Using the same strategy for HUNL as Cepheus did is out of the question. Rather, taking an educated guess or using intuition based on the previous play (referred to as Continual Re-solving in the paper) is a method which can better handle this gargantuan game. For more information check out the DeepStack website.